Machine Learning Engineer Want to work on the systems that make LLMs fast, scalable and efficient in production? A Stanford-spun AI company is hiring anAI Inference Engineerto help build and optimise the infrastructure powering its production AI platform. This is a deeply technical role focused on squeezing more performance fromGPUs, distributed systems and LLM inferenceat scale. What you'll be working onArchitect high-performance infrastructure forLLM inference Optimiselatency, throughput and costacross production workloads Scale inference acrossmulti-GPU and multi-node environments Improve GPU scheduling, allocation and utilisation Profile bottlenecks acrosscompute, memory and networking Optimisebatching, quantisation, KV caching and parallelism Scale GPU workloads across Kubernetes infrastructure
What we're looking for:
You'll come from anML systems, distributed computing, HPC or performance engineeringbackground and be comfortable working close to the hardware.
StrongPython and/or C++, PyTorch and GPU computing experience are key.
Experience withCUDA, NCCL, Triton, vLLM, TensorRT-LLM or SGLangwould be particularly relevant.
You should be comfortable reasoning about howGPU utilisation, memory bandwidth, communication overhead and model architectureinteract to impact inference performance.
